feat(agent): optional int8 vector index, re-scored against exact embeddings

`MemoryConfig::quantized_index` stores the HNSW index's own copy of the
embeddings as i8 rather than f32. At 100k x 384 that takes the index from
266 to 123 MiB and the whole reopened store from 399 to 256 MiB — 2.72x
to 1.74x the raw vectors, the largest remaining item in the footprint.

Quantised distances are approximate and `ef` cannot compensate, because
the loss is in the distances rather than in the graph: recall@10 tops out
at 0.967 against f32's 0.9995 and does not move between ef=128 and
ef=256. The store already holds the exact embeddings, though, so when the
index is quantised the query path re-scores the candidate pool against
them before fusion. That restores recall (0.9940 vs 0.9945 at ef=64) and
costs about 13% of QPS.

Off by default: it trades query speed for memory and which side is worth
more depends on the deployment. The flag is persisted in `/meta`, so a
reopened store does not silently revert to four times the index memory,
and the sidecar graph is rehydrated into the configured storage.

Also on the CLI as `create --quantized-index`.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>

README.md

@@ -432,6 +432,13 @@ ClawhDF5's agent memory design draws from 15+ recent papers:
 | `agent` | no | Full agent memory layer |
 | `float16` | **yes** | Half-precision embedding storage (2× compression) |
 | `hnsw` | **yes** | HNSW approximate vector index for `hybrid_search` (via `clawhdf5-ann`); disable for an exact linear scan |
+
+`MemoryConfig::quantized_index` (off by default) stores the HNSW index's own
+copy of the embeddings as `i8`, roughly halving a loaded store's memory
+(2.72x -> 1.74x the raw vectors at 100k x 384). Quantised distances are
+approximate, so the query path re-scores the candidate pool against the exact
+embeddings the store already holds — recall matches the `f32` index, at about
+13% fewer queries per second. See `BENCHMARKS.md`, "Quantising the index copy".
 | `parallel` | no | Rayon parallel search |
 | `fast-math` | no | BLAS matrix-vector multiply |
 | `accelerate` | no | Apple Accelerate / AMX (macOS) |